Authors
Kyungim Baek, Bruce A Draper, J Ross Beveridge, Kai She
Publication date
2002/3/8
Conference
JCIS
Pages
824-827
Description
Over the last ten years, face recognition has become a specialized applications area within the field of computer vision. Sophisticated commercial systems have been developed that achieve high recognition rates. Although elaborate, many of these systems include a subspace projection step and a nearest neighbor classifier. The goal of this paper is to rigorously compare two subspace projection techniques within the context of a baseline system on the face recognition task. The first technique is principal component analysis (PCA), a well-known “baseline” for projection techniques. The second technique is independent component analysis (ICA), a newer method that produces spatially localized and statistically independent basis vectors. Testing on the FERET data set (and using standard partitions), we find that, when a proper distance metric is used, PCA significantly outperforms ICA on a human face recognition task. This is contrary to previously published results.
Total citations
Scholar articles
K Baek, BA Draper, JR Beveridge, K She - JCIS, 2002